• Hospitals have a complex sequence of patient care practices that must be coordinated to deliver optimum service. Patients often arrive through the emergency room and are evaluated to determine if they should be discharged or admitted to the hospital. Once they are admitted they are assigned to a specialty ward of care focused on their treatment need. They will remain in that ward until they are released or transferred to a different service ward. They often wait to be transferred into a specialty ward until the space becomes available. When they are released from the hospital there is some time required to clean and reset the room for the next patient.
  • Movement through the hospital system drives a complex set of dependencies including workforce planning and space management logistics across diverse organizations. The ability to meet constituent user need and to have a view of the details and the over-arching status is a challenge.
  • The hack project is to define a software application and user interface to orchestrate this complex process.


People can understand what is going on and take action to update a centralized system and view details at the service ward and at the air traffic controller level
  • Describe
    • Patient occupancy, arrivals and departures at the service ward, Specialty and enterprise, and queue depth of patients waiting to move to the next destination
  • Increase
    • The queue depth while reducing the wait time. Optimize throughput.
  • Visualize
    • Current state and pending admitting and discharging actions and the time to move through the system
  • Enable
    • Staff to add, assign and modify patient status
    • Ward managers to  add, assign and modify workforce allocation
  • Simulate
    • Replay data for arrivals and departures through the system


  • Produce
    • User interface to add and manage data
    • An application to process data and business logic
    • A database to store and retrieve data
    • A visual display dashboard to summarize ward level and business level data
    • A data replay tool to drive information through the system
    • Arrival and departure events and rates
    • Statistical and predictive analysis
  • Use any technology of your choosing


  • The most dynamic handling of the micro and macro view of hospital patient flows
    • Most creative use of data
    • Most informative insight from data
    • Most artistic rendering of data
    • Most advanced and efficient use of the technology implementation


  • A three month (dates of your choosing) resident membership and workspace at TechSpring's world-class facilities in Springfield, MA to all members of the winning team!
  • Opportunity to present at a Tap into TechSpring monthly meeting that includes healthcare professionals and technology innovators (specific month TBD)
  • Opportunity to present to representatives of Baystate Health leadership team 
  • A professional video production of the team and the solution
  • Certificate of acknowledgement award
Additional Information:
Specialties and Wards
•SL0 – Specialty 0 = Emergency Room 1 ward. Low predictability.
–# Beds per ward 100
•SL1 - Specialty 1 = 1 ward. Low predictability.
–# Beds per ward 28
•SL2 - Specialty 2 = 5 wards. Medium predictability.
–# Beds per ward 18, 32, 32, 32, 12
•SL3 - Specialty 3 = 2 wards. High predictability.
–# Beds per ward 16, 16
•SL4 - Specialty 4 = 9 wards. Low predictability.
–# Beds per ward 23, 9, 34, 34, 23, 32, 24, 24, 12
•SL5 - Specialty 5 = 2 wards. Medium predictability.
–# Beds per ward 37, 9
•SL6 - Specialty 6 = 1 ward. High predictability.
–# Beds per ward 32
•SL7 - Specialty 7 = 3 wards. Medium predictability.
–# Beds per ward 44, 28, 16
Specialty Ward Micro View
•Patient data fields
–Specialty ward (string)
–Bed letter (string)
–Room number (Integer)
–Patient name (person)
–Patient ID (integer)
–Team assignment (group ID)
–Case Manager (person)
–Attending Physician (person)
–Arrival date and time (date string)
–Projected Discharge (date string)
–Actual Discharge Date and time (date string)
–Internal Transfer Out (string) (moving out to a different ward or Specialty)
–Internal Transfer In (string) (coming to a ward from a different ward)
•Specialty ward data fields
–ward manager (person)
–Total beds (integer) (Capacity # beds open + used + closed)
–Admitting next patient (queue)
–Bed capacity (real beds)
–Escalation flags that limit occupancy such as insufficient staff or room is delayed or private room
–Hotels (in the ward, not in a room, awaiting transfer out to another ward or discharge)
–Expected admissions by history
–Emergency Department holds (ED) awaiting admittance
•Calculated fields
–Open beds = capacity – census – closed beds
–Used beds = census = beds inhabited by patients
–Closed beds = “out of service” beds due to readiness issues
–Utilization factor
–Predictor percentage (what actually happened versus what was predicted to happen)
–History expected / projections
–END PROJECTION = Open beds + Discharges, + Internal Transfer Out – Internal Transfer In – Hotels –Expected Admissions by history – ED Holds
Hospital Macro View
•Patient progress manager (air traffic controller)
–Summary of specialties and their respective service wards
•Details of ins, outs, occupancies, vacancies, efficiencies, etc
–Workforce (Physician, Nurses, Medical Assistants, Leaders, Managers)
–Environmental services bed reset teams
–Pharmacy, Emergency Room admitting
Hierarchy Analogy
•A patient (passenger) is the smallest unit (chair)
•A room (aisle) has 1 or more beds (a seating row)
•A team (crew) services 7 beds (Economy vs. business)
•A ward (flight) is part of a service Specialty (an Airline)
•A Specialty (airline) has 1 or more wards (multiple flights)
•A hospital (airport) has 1 or more Specialties (airlines)
•An emergency room (airport infrastructure) is a mini hospital that feeds Specialties (bag check, security, gate)
–Airport dashboards for arrivals, departures, baggage claim
–The data, process and flow management to optimize the user experience
•Support teams are assigned a maximum of 7 beds per ward
•Patients may be assigned to a ward and freely reassigned (patient out -> patient in) within the same specialty
•They should be tracked as having been reassigned to a different ward. This is deemed optimization of beds within a specialty.
•Assume about 100 new patients are admitted per day and the average patient stay is 5 days
•Patient discharge may take several hours to process and bed reset time may take several hours to process and that time compounds the delays in the queue process. Transfers between wards in the same unit or between specialties generates transfer outs and transfer ins queue impact.
•Mock up patient arrival rates and patient departure rates with a variable predictability factor to balance the system and queue depths. Provide tunable settings tweak flows.
Optimization objectives
–Move people from place to place to optimize the distribution of patients within a Specialty. Optimize how different Specialties are managed and how patients are moved through the system
–Predict discharges versus what actually gets discharged (drive more granular time window)
–Manage occupancy and pending patient requests as in patients on hold pending servicing e.g. in the corridor
–Automate current manual and static system
–Manage the patient volume and inflow at the unit level
–Address the occupancy problem, queuing problem and throughput problem efficiency
–Keep the pressure of the queue depth at the correct level maximize occupancy e.g. occupancy queues are slightly full
–Understand the trends in the discharge of patients
–Determine the metrics you should pay attention to to handle workflows and expectations


Office Hours
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  • 8am-11am at TechSpring booth  Jill McCormick, Innovation Manager @ TechSpring
  • 8am-1pm, 6-7pm at TechSpring Booth, Jacob Lindeman, Project Advisor and Mentor @TechSpring, Valley Venture Mentors, Worcester Polytechnic Ins
  • 8-10am  at TechSpring Booth, Jacob Lindeman, Project Advisor and Mentor @TechSpring, Valley Venture Mentors, Worcester Polytechnic Ins